code: def _load_runnable_with_steps(
      file_path: Union[Path, str], model_type: str
  ):
      """Load the model
  
      Args:
          file_path: Path to file to load the model from.
          model_type: Type of the model to load.
      """
      from langchain.schema.runnable import (
          RunnableParallel,
          RunnableSequence,
      )
  
      # Convert file to Path object.
      load_path = (
          Path(file_path) if isinstance(file_path, str) else file_path
      )
      if not load_path.exists() or not load_path.is_dir():
          raise MlflowException(
              f"File {load_path} must exist and must be a directory "
              "in order to load runnable with steps."
          )
  
      steps_conf_file = load_path / _RUNNABLE_STEPS_FILE_NAME
      if not steps_conf_file.exists():
          raise MlflowException(
              f"File {steps_conf_file} must exist in order to load runnable with steps."
          )
      steps_conf = _load_from_yaml(steps_conf_file)
      steps_path = load_path / _STEPS_FOLDER_NAME
      if not steps_path.exists() or not steps_path.is_dir():
          raise MlflowException(
              f"Folder {steps_path} must exist and must be a directory "
              "in order to load runnable with steps."
          )
  
      steps = {}
      for step in os.listdir(steps_path):
          config = steps_conf.get(step)
          # load model from the folder of the step
          runnable = _load_model_from_path(
              os.path.join(steps_path, step), config
          )
          steps[step] = runnable
  
      if model_type == RunnableSequence.__name__:
          steps = [
              value
              for _, value in sorted(
                  steps.items(), key=lambda item: int(item[0])
              )
          ]
          return runnable_sequence_from_steps(steps)
      if model_type == RunnableParallel.__name__:
          return RunnableParallel(steps)
  
file_path: mlflow/mlflow/langchain/runnables.py
imports: import os
  from pathlib import Path
  from typing import Union
  import cloudpickle
  import yaml
  from mlflow.exceptions import MlflowException
  from mlflow.langchain.utils import _BASE_LOAD_KEY, _CONFIG_LOAD_KEY, _MODEL_DATA_FOLDER_NAME, _MODEL_DATA_KEY, _MODEL_DATA_PKL_FILE_NAME, _MODEL_DATA_YAML_FILE_NAME, _MODEL_LOAD_KEY, _MODEL_TYPE_KEY, _RUNNABLE_LOAD_KEY, _UNSUPPORTED_MODEL_ERROR_MESSAGE, _load_base_lcs, _load_from_json, _load_from_pickle, _load_from_yaml, _save_base_lcs, _validate_and_wrap_lc_model, base_lc_types, custom_type_to_loader_dict, lc_runnable_branch_type, lc_runnable_with_steps_types, lc_runnables_types, picklable_runnable_types
label_desc: Use image node_id 3 for calling a global function with example usage: _load_runnable_with_steps(file_path, model_type) and returns: runnable_sequence_from_steps, RunnableParallel